> One thing I have little doubt about is that pypy needs a "story" to
> makes wrapping of fortran/c/c++ libraries easy, because otherwise few
> people in the scientific community will be interested. For better or
> worse, there are tens of millions of lines of code written in those
> languages, and a lot of them domain specific (you will not write a
> decent FFT code without knowing a lot about its implementation
> details, same for large eigen values problems). There needs to be some
> automatic wrappers generators. Scipy alone easily wraps thousand if
> not more functions written in fortran.
Yes, we're well aware of that and we don't plan to rewrite this
existing codebase. As of now you can relatively easily call C/fortran
from RPython and compile it together with PyPy. While PyPy does not
have a C API, arrays are still "raw memory" which means you can pass
pointers to the underlaying C libraries. We don't have (yet?)
automatic binding generation but this is for later.
The main thing is that we want to provide something immediately
useful. That is a numpy which maybe does not integrate (yet) with the
entire ecosystem, but is much faster on both array computations and
pure python iterations, ufuncs etc. This would be very useful since
you don't need to use Cython or any other things like this to provide
working code and it already caters for some group of people.
Cheers,
fijal